In-situ soil texture classification and physical clay content measurement based on multi-source information fusion

Chao Meng, Wei Yang, Xinjian Ren, Dong Wang, Minzan Li

Abstract


Soil texture is one of the most important soil characteristics that affect soil properties. Rapid acquisition of soil texture information is of great significance for accurate farmland management. Traditional soil texture analysis methods are relatively complicated and cannot meet the requirements of temporal and spatial resolution. This research introduced a self-developed vehicle-mounted in-situ soil texture detection system, which can predict the type of soil texture and the particle composition of the texture, and obtain real-time data during the measurement process without preprocessing the soil samples. The detection system is mainly composed of a conductivity measuring device, a camera, an auxiliary mechanical structure, and a control system. The soil electrical conductivity (ECa) and the texture features extracted from the surface image were input into the embedded model to realize real-time texture analysis. In order to find the best model suitable for the detection system, measurements were carried out in three test fields in Northeast and North China to compare the performance of different models applied to the detection system. The results showed that for soil texture classification, ExtraTrees performed best, with Precision, Recall, and F1 all being 0.82. For particle content of soil texture prediction, the R2 of ExtraTrees was 0.77, and RMSE and MAPE were 74.72 and 39.58. It was observed that ECa, Moment of inertia, and Entropy had larger weights in the drawn model influence weight map, and they are the main contributors to predicting soil texture. These results showed the potential of the vehicle-mounted in-situ soil texture detection system, which can provide a basis for fast, cost-effective, and efficient soil texture analysis.
Keywords: soil texture, soil sensor, electrical conductivity, soil surface image
DOI: 10.25165/j.ijabe.20231601.6918

Citation: Meng C, Yang W, Ren X J, Wang D, Li M Z. In-situ soil texture classification and physical clay content measurement based on multi-source information fusion. Int J Agric & Biol Eng, 2023; 16(1): 203–211.

Keywords


soil texture, soil sensor, electrical conductivity, soil surface image

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References


Shahadat Hossain M, Mustafizur Rahman G K M, Saiful Alam M, Mizanur Rahman M, Solaiman A R M, Baset Mia, M A. Modelling of soil texture and its verification with related soil properties. Soil Research, 2018; 56(4): 421–428.

Coblinski J A, Inda A V, Dematte J A M, Dotto A C, Gholizadeh A, Giasson E. Identification of minerals in subtropical soils with different textural classes by VIS-NIR-SWIR reflectance spectroscopy. CATENA, 2021; 203: 105334. doi: 10.1016/j.catena.2021.105334.

Hollis L O, Turner R E. The tensile root strength of spartina patens varies with soil texture and atrazine concentration. Estuaries and Coasts, 2019; 42: 1430–1439.

Hegde R, Bhaskar B P, Niranjana K V, Ramesh Kumar S C, Ramamurthy V, Srinivas S, et al. Land evaluation for groundnut (Arachis hypogaea L.) production in Pulivendula tehsil, Kadapa district, Andhra Pradesh, India. Legume Research, 2017; 42(3): 326–333.

Li H R, Liu B, Wang R X, Liu W, Fang Y, Yang D L, et al. Particle-size distribution affected by testing method. Journal of Desert Research, 2018; 38(3): 619–627. (in Chinese)

Wang W P, Liu J L, Zhang J B, Li X P. Evaluation and correction of measurement using diffraction method for soil particle size distribution. Transactions of the CSAE, 2014; 30(22):163–169. (in Chinese)

Stefani Fae G, Montes Felipe, Bazilevskaya E, Masip Ano R, Kemanian A R. Making soil particle size analysis by laser diffraction compatible with standard soil texture determination methods. Soil Science Society of America Journal, 2019; 83(4): 1244–1252.

Zhu Y, Zhang Z D, Liu C, Zhang X. Comparison of laser diffraction method and pipette method on soil particle size distribution determination - a case study of variously degraded kastanozem. Research of Soil and Water Conservation, 2018; 25(3): 62–67, 204. (in Chinese)

Fisher P, Aumann C, Chia K, O'Halloran N, Chandra S. Adequacy of laser diffraction for soil particle size analysis. Plos One, 2017; 12(5): e0176510. doi: 10.1371/journal.pone.0176510.

Swetha R K, Bende P, Singh K, Gorthi S, Biswas A, Li B, et al. Predicting soil texture from smartphone-captured digital images and an application. Geoderma, 2020; 376: 114562. doi: 10.1016/j.geoderma.2020.114562.

de Oliveira Morais P A, de Souza D M, Madari B E, de Oliveira A E. A computer-assisted soil texture analysis using digitally scanned images. Computers and Electronics in Agriculture, 2020; 174: 105435. doi: 10.1016/j.compag.2020.105435.

de Oliveira Morais P A, de Souza D M, de Melo Carvalho M T, Madari B E, de Oliveira A E. Predicting soil texture using image analysis. Microchemical Journal, 2019; 146: 455–463.

Sudarsan B, Ji W J, Biswas A, Adamcuk V. Microscope-based computer vision to characterize soil texture and soil organic matter. Biosystems Engineering, 2016; 152: 41–50.

García-Tomillo A, Mirás-Avalos J M, Dafonte-Dafonte J, Paz-González A. Mapping soil texture using geostatistical interpolation combined with electromagnetic induction measurements. Soil Science, 2017; 182(8): 278–284.

Andrade R, Silva S H G, Faria W M, Poggere G C, Barbosa J Z, Guiherme L R G, et al. Proximal sensing applied to soil texture prediction and mapping in Brazil. Geoderma Regional, 2020; 23: e00321. doi: 10.1016/j.geodrs.2020.e00321.

Kargas G, Londra P, Sgoubopoulou A. Comparison of soil EC values from methods based on 1:1 and 1:5 soil to water ratios and ECe from saturated paste extract based method. Water, 2020; 12(4): 1010. doi: 10.3390/w12041010.

Wu K N, Zhao R. Soil texture classification and its application in China. Acta Pedologica Sinica, 2019; 56(1): 227–241.

Pei X S, Meng C, Li M Z, Yang W, Zhou P. Measurement of soil electrical conductivity based on direct digital synthesizer (DDS) and digital oscilloscope. Int J Agric & Biol Eng, 2019; 12(6):162–168.

Pare S, Bhandari A K, Kumar A, Singh G K. An optimal color image multilevel thresholding technique using grey-level co-occurrence matrix. Expert Systems with Applications, 2017; 87: 335–362.

Awais M, Ghayvat H, Pandarathodiyil A K, Ghani W M N, Ramanathan A, Pandya S, et al. Healthcare professional in the loop (HPIL): Classification of standard and oral cancer-causing anomalous regions of oral cavity using textural analysis technique in autofluorescence imaging. Sensors, 2020; 20(20): 5780. doi: 10.3390/s20205780.

Kulkarni A, Carrion-Martinez I, Dhindsa K, Alaref A A, Rozenberg R, van der Pol C B. Pancreas adenocarcinoma CT texture analysis: Comparison of 3D and 2D tumor segmentation techniques. Abdominal Radiology, 2021; 46(4): 1027–1033.

Guan H X, Liu H J, Meng X T, Luo C, Bao Y L, Ma Y Y, et al. A quantitative monitoring method for determining maize lodging in different growth stages. Remote Sensing, 2020; 12(19): 3149. doi: 10.3390/rs12193149.

Wang H P, Hui L. Classification recognition of impurities in seed cotton based on local binary pattern and gray level co-occurrence matrix. Transactions of the CSAE, 2015; 31(3): 236–241. (in Chinese)

Liu Z. Image-based prediction of soil roughness and soil bulk density. Master dissertation. Beijing: China Agricultural University, 2020; pp.37–46. (in Chinese)

Liu Z, Yang W, Li M Z, Zhou P, Yao X Q, Chen Y Q, et al. Soil roughness measuring system combined with image processing. IFAC-PapersOnLine, 2018; 51(17): 689–694.

Dou P, Chen Y B. Remote sensing imagery classification using AdaBoost with a weight vector (WV AdaBoost). Remote Sensing Letters, 2017; 8(8): 733–742.

Ge X, Sun J, Lu B, Chen Q S, Xun W, Jin Y T. Classification of oolong tea varieties based on hyperspectral imaging technology and BOSS-LightGBM model. Journal of Food Process Engineering, 2019; 42(8): e13289. doi: 10.1111/jfpe.13289.

Huang M W, Chen C W, Lin W C, Ke S W, Tsai C F. SVM and SVM ensembles in breast cancer prediction. Plos One, 2017; 12(1): e0161501. doi: 10.1371/journal.pone.0161501.

Wu M, Lyu B L. Prediction of viscosity of ternary tin-based lead-free solder melt using BP neural network. Soldering and Surface Mount Technology, 2020; 32(3): 173–180.

Samat A, Liu S C, Persello C, Li E Z, Miao Z L, Abuduwaili J. Evaluation of ForestPA for VHR RS image classification using spectral and superpixel-guided morphological profiles. European Journal of Remote Sensing, 2019; 52(1):107–121.

Mantas C J, Castellano J G, Moral-García S, Abellán J. A comparison of random forest based algorithms: random credal random forest versus oblique random forest. Soft Computing, 2019; 23(5): 10739–10754.

Ou X, Pan W, Xiao P. In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). International Journal of Pharmaceutics, 2014; 460(1-2): 28–32.

Demattê J A M, Alves M R, da Silva Terra F, Bosquillia R W D, Fongaro C T, da Silva Barros P P. Is it possible to classify topsoil texture using a sensor located 800 km away from the surface? Revista Brasileira De Ciência Do Solo, 2016; 40: e0150335. doi: 10.1590/ 18069657rbcs20150335.

Vibhute A D, Kale K V, Dhumal R K, Mehrotra S C. Soil type classification and mapping using hyperspectral remote sensing data. In: 2015 International Conference on Man and Machine Interfacing (MAMI), Bhubaneswar: IEEE, 2016; pp.1–4. doi: 10.1109/MAMI.2015.7456607.

Taghizadeh-Mehrjardi R, Sarmadian F, Minasny B, Triantafilis T, Omid M. Digital mapping of soil classes using decision tree and auxiliary data in the ardakan region. Arid Land Research and Management, 2014; 28(2): 147–168.

Dharumarajan S, Hegde R. Digital mapping of soil texture classes using Random Forest classification algorithm. Soil Use and Management, 2020; 38(1): 135–149.

Barman U, Dev Choudhury R. Soil texture classification using multi class support vector machine. Information Processing in Agriculture, 2019; 7(2): 318–332.

Aitkenhead M, Cameron C, Gaskin G, Choisy B, Coull M, Black H. Digital RGB photography and visible-range spectroscopy for soil composition analysis. Geoderma, 2018; 313: 265–275.

Qi L, Adamchuk V, Huang H H, Leclerc M, Jiang Y, Biswas A. Proximal sensing of soil particle sizes using a microscope-based sensor and bag of visual words model. Geoderma, 2019; 351:144–152.

Sudarsan B, Ji W J, Adamchuk V, Biswas A. Characterizing soil particle sizes using wavelet analysis of microscope images. Computers and Electronics in Agriculture, 2018; 148: 217–225.

Sudarsan B, Ji W J, Biswas A, Adamchuk V. Microscope-based computer vision to characterize soil texture and soil organic matter. Biosystems Engineering, 2016; 152: 41–50.

Benedet L, Faria W M, Silva S H G, Mancini M, Demattê J A M, Guilherme L R G, et al. Soil texture prediction using portable X-ray fluorescence spectrometry and visible near-infrared diffuse reflectance spectroscopy. Geoderma, 2020; 376: 114553. doi: 10.1016/ j.geoderma.2020.114553.

Wang S Q, Li W D, Li J, Liu X S. Prediction of soil texture using FT-NIR spectroscopy and PXRF spectrometry with data fusion. Soil Science, 2013; 178(11): 626–638.

Kelley J, Higgins C W, Pahlow M, Noller J. Mapping soil texture by electromagnetic induction: a case for regional data coordination. Soil Science Society of America Journal, 2017; 81(4): 923–931.

Bañón S, Álvarez S, Bañón D, da Ortuño M F, Sánchez-Blanco M J. Assessment of soil salinity indexes using electrical conductivity sensors. Scientia Horticulturae, 2021; 285: 110171. doi: 10.1016/ j.scienta.2021.110171.




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